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Climate 2019, 7(9), 103; https://doi.org/10.3390/cli7090103

Article
The Impacts of Climate Change on Road Traffic Accidents in Saudi Arabia
1
Department of Finance, COB, King Abdulaziz University, Rabigh 21911, Saudi Arabia
2
School of Economics, Finance & Banking, Universiti Utara Malaysia, Kedah 06010, Malaysia
*
Author to whom correspondence should be addressed.
Received: 15 July 2019 / Accepted: 28 August 2019 / Published: 30 August 2019

Abstract

:
The potential costs of road traffic accidents (RTAs) to society are immense. Yet, no study has attempted to examine the impact of climate change on RTAs in Saudi Arabia, though RTA-leading deaths are very high, and the occurrence of climatic events is very frequent. Therefore, this study aims to assess the impact of climate change on RTAs in Saudi Arabia and to recommend some climate change mitigation and adaptation policies to make roads safe for all. This study employed annual data from 13 regions of Saudi Arabia, from 2003 to 2013. The data were analyzed on the basis of panel regression models—fixed effect, random effect, and the pooled ordinary least square. The findings show that temperature, rainfall, sandstorms, and number of vehicles were statistically and significantly responsible for RTAs in Saudi Arabia in the study period. This study also found that RTAs both inside and outside cities significantly caused injuries, but only RTAs inside cities significantly caused death. Furthermore, the death from RTAs injuries was found to be statistically significant only for motor vehicle accidents. The findings will assist policymakers in taking the right courses of action to mitigate the negative impacts of climate change through understanding climate influence on RTAs.
Keywords:
road traffic accidents; climate change; injuries; death; mitigation; adaptation; saudi arabia

1. Introduction

Road traffic accident (RTA) is defined as a situation caused by the collision of one or more motorized vehicles, such as cars and motorcycles. The consequences of RTAs can be injuries, property damages, deaths, and congestion, disruption, and delays to public transport [1]. According to the World Health Organization [2], road accidents have a high cost in terms of lives, which is equal to about 1.25 million people annually. In addition, the leading cause of death is highway traffic injuries for people who are 15–29 years old, and highway traffic injuries account for more than 300,000 deaths.
Furthermore, RTAs are now in the ninth position when it comes to the cause of death around the world, and are expected to be in the fifth position by 2020 [3]. Moreover, 20–50 million people are injured or disabled globally in road crashes each year [4]. According to the World Bank [5], the highest number of road accidents occurs in low- and middle-income countries (85%). However, according to the Association for Safe International Road Travel [6], road crashes cost USD $518 billion globally, costing individual countries 1–2% of their annual GDP.
WHO [7] reported that safe mobility could reduce RTAs significantly in urban and rural areas. Safe mobility is also highly important as it supports human rights through the protection of other peoples’ lives during driving [8]. Moreover, the policies of transportation enhance safe mobility to achieve resilient and sustainable transport control [9]. Therefore, it is important to understand the causes of RTAs in different environmental, socioeconomic, and geographical contexts.
To reduce RTAs, a large number of research studies have been conducted from an engineering perspective, aiming at improving the vehicle safety systems and designing highways and safety policies [10]. The improvement of highways safety policies, however, is costly; most of the time, the awareness of safety of the drivers varies among people, and it is hard to anticipate that this scenario would change [11]. Previous studies also investigated the influence of various factors on RTAs, such as driver’s age, income level, judgment, skill, attention, fatigue, experience, high speed, vehicle’s design, manufacture, and maintenance, and environmental conditions, such as lighting, sandstorms, temperature, and precipitations [12,13,14,15,16,17,18,19,20]. However, out of the many factors that cause RTAs, climatic factors are still the least studied in the literature and therefore need to be addressed. In desert areas, the road environment is generally more vulnerable to increases in the intensity and frequency of hot days, storm activities, and sea level. The impact of climate change on RTAs has not been sufficiently investigated in Middle Eastern countries and, in particular, Saudi Arabia, though the climate of Saudi Arabia is extremely arid, mostly characterized by very low rainfall, very hot and dry summer, and severe evapotranspiration. In addition, as emphasized in Kingdom of Saudi Arabia (KSA) vision 2030 [21], Saudi Arabia is committed to effectively implementing sustainable development and, to this aim, has made steady progress in various sectors and areas. Even though climate change has a significant impact on RTAs [22,23], to date no empirical study has investigated the impact of climate change on RTAs, whose potential costs to society are immense. Therefore, to overcome this literature gap, this study aimed to assess empirically the impact of climate change on the RTAs in Saudi Arabia as an example of climate change-affected arid region.

2. Literature Review

2.1. Road Traffic Accidents in Saudi Arabia

The literature shows that Saudi Arabia has a high number of road accidents and it is in the second place for road accidents in the Gulf area after Iran [7]. In Saudi Arabia, about 14.8 people died per 1000 vehicles in 2004 compared to the United Kingdom where about 1.5 people died per 1000 vehicles [24]. Furthermore, road accidents were found to be one of the main reasons of death of young adults, accounting for 49% of death cases for people below 30 years of age [7,25]. Moreover, RTAs are considered a threat to health, causing 19 deaths per day and 4 cases of injuries each hour in Saudi Arabia [13]. According to the Saudi Ministry of Interior [26], the number of road traffic accidents was about 550,000 in 2010 and caused roughly 7200 fatalities, implying that there were 20 RTAs for every 1000 people and 1 fatality for every 76 RTAs. Furthermore, RTA is a national problem that adversely affects the economy and societal fabric because the young and economically productive age groups are the most affected by RTAs [27]. The total loss caused by RTAs in Saudi Arabia is recorded to be roughly between 2.2% and 9% of the national income compared to that of industrialized countries that is between 1% and 2% [28,29]. The annual cost of RTAs has reached $6 billion [30]. As a result of the launch of some road safety initiatives, such as strict new punishments for traffic violations, including fines and lengthy prison sentences for causing injury or death, and a crackdown on mobile phone usage, the number of road traffic accidents and injuries has decreased in the last few years [31]. RTAs still cause an average of 20 deaths per day, which is very high by all standards and alarming compared to that of other developed European countries (5.27 death/day) [32]. Most of the road safety initiatives and policies are concerned with institutional reforms and regulation, but the essential issues of climate change mitigation and adaptation are ignored. Therefore, a research investigation conducted in the Saudi Arabian context will help the policymakers to take proper policy actions to reduce RTAs; in addition, the adopted policy might be applicable to similar countries in this region.

2.2. Climate Change in Saudi Arabia

The recent change in the climate of Saudi Arabia indicates that the country is already facing variations of temperatures, dust/sand storms, and rainfalls [33]. Saudi Arabia has a desert climate characterized by extremely high temperatures during the day, a sudden drop in temperature at night, very low annual rainfall, and average humidity. This country has mainly two seasons: summer and winter. The average summer temperature is around 45 °C (from May to September) for the majority of regions, but it can be as high as 54 °C. In the month of February, March, April, and November, it is neither too cold nor too hot. During peak winter (December to January), the temperature usually drops at night quite rapidly. The annual rainfall is extremely low, especially in the central region. A recent study was conducted by Tarawneh and Chowdhury [34] to examine the trends of climate change in Saudi Arabia and reported an increase in temperature in all regions and a decrease of rainfall in many regions. Almazroui et al. [35] reported that the maximum, mean, and minimum temperatures are increasing significantly at rates of 0.71, 0.60, and 0.48 °C per decade, respectively. At the same time, Chowdhury and Al-Zahrani [36] predicted an increase in rainfall by 15–25 mm/year in the central, western, and eastern regions by 2050. Although send/dust storms occur throughout the entire year in Saudi Arabia [37], on the local scale, these events are known to be variable in space and time. For instance, Albugami et al. [37] reported that the eastern and western parts of Saudi Arabia have been experiencing an increase in dust storm events over time, while the occurrence of dust storm events has been decreasing over time in the northern part. Overall, the eastern part of Saudi Arabia experiences the highest number of dust storms per year (i.e., 10 to 60 events), and the west part has fewer dust storm events (i.e., 5 to 15 events per year).

2.3. Road Traffic Accidents and Climate Change

Previous studies have investigated the influence of various factors that affect both death and injury severity caused by road traffic accidents. The causes of road crashes can be attributed to human, vehicular, and environmental factors, like becoming busy with different activities while driving (such as usage of mobile phones), failure to see the surroundings of the vehicles [38], negative emotions [39], intake of alcohols, fatigue, medications, drowsiness [20], age, and gender of the drivers [12,13].
Besides the above-mentioned factors, climate change, especially hazardous weather (e.g., wind speed, precipitation, rain, snow, temperature, fog, etc.) can increase the number of road accidents [40,41,42,43,44,45]. Focusing on the United Kingdom, Edward [43] examined the correlation between weather and road accidents. The results showed a positive and significant association between the indicators of weather and road crashes in the UK, and this result varied concerning seasons and places. Another study was conducted by Edward [44] in England and Wales and concluded that there was a significant relationship between road accidents and hazards of weather.
In addition, Andrey et al. [46] found that the hazards of weather significantly affected the risk of accidents in middle-sized cities of Canada and that the impacts of snowfall were higher than the effects of rainfall. Moreover, Eisenberg and Warner [47] also identified snowfall as a hazardous weather condition. This result is consistent with the findings of Fridstrom [48], who focused on Norway. Furthermore, Musk [49] claimed that the association between the rates of accidents and thick fog was significant and positive. This means that highly poor road visibility results in more RTAs [50,51].
Based on the existing literature, it is observed that the impacts of rainfall on RTA vary widely across the world, but the majority of the studies have proposed that a greater rate of rainfall leads to more accidents [52,53,54,55,56,57,58,59]. For example, Jaroszweski and McNamara [52] reported a positive linear relationship between rainfall amount and the number of road accidents in urban areas of Manchester and Greater London in the UK. In addition, Fridstrom et al. [59] confirmed that rain increased accidents significantly due to loss of vehicle control and lower visibility. Andrey et al. [46] confirmed that an increase in the number of precipitations increased RTAs and injuries by 75%. In contrast, a few studies have reported that there is a negative association between rainfall and RTA, as drivers adjusts their behavior during rainfall [44,60,61]. For example, Mondal et al. [60] reported that accident severity decreased significantly during rain compared with fine weather in India. They also stated that the extra care of drivers during rainy days, low vehicle speed due to traffic congestion, and runoff effect could be the reasons for this negative relationship between rainfall and RTAs.
According to several researchers, temperature plays a strong role in road accidents, but there is no common definable relationship between temperature and RTAs [62,63,64]. Some studies have reported that low temperatures are associated with more accidents [65,66]; on the other hand, the majority of the studies have reported that high temperatures are the root cause of road accidents, and more time of sunlight leads to more crashes. This means that high temperatures influence RTAs significantly and positively [57,63,67,68,69,70].
Only a few studies have been conducted to identify the relationship between dust/sand storms and RTAs [71,72,73,74,75,76,77,78]. Most of these studies have reported a positive and significant relationship between dust/sand storms and RTAs. For example, Shoemaker [73] found that sandstorm was the third ranked weather event in Arizona for RTAs and led to most of RTAs deaths and injuries from 1955 to 2004. Sandstorm-related accidents occur statewide, with a concentration on interstate highways, especially those with the greatest traffic density. Solomon [71] stated that dust/sand storms were powerful enough to cause extremely low visibility, resulting in severe road traffic accidents and damage to the economy. Conversely, other researches have illustrated that there is a negative relationship between weather and road traffic accident rate [79].
Based on the review of the impacts of temperature, rainfall, and dust/sand storms on RTAs across the world, the following research questions underpin this study:
  • Is there a a positive relationship between climate change (such as rainfall, temperature, dust/sand storms) and RTAs?
  • Is the impact of climate change on RTAs higher inside cities than outside cities?

3. Materials and Methods

3.1. Data

To investigate the impact of climate change on road traffic accidents in Saudi Arabia, this study employed annual data from 13 regions from 2003 to 2013 and 143 observations. The data were retrieved from various sources such as World Bank, Ministry of Interior and Presidency of Meteorology and Environment of Saudi Arabia, Our World in Data, and Global Health Data Exchange [5,80,81,82,83].

3.2. Variables and Model

This article estimated how the climate change variables influenced road traffic accidents, considering empirical evidence from Saudi Arabia and using the following models:
ICA it = α 0 + β 1 ATM it + β 2 ARF it + β 3 FST it + β 4 TVH it + ε it
OCA it = α 0 + β 1 ATM it + β 2 ARF it + β 3 FST it + β 4 TVH it + ε it
TAC it = α 0 + β 1 ATM it + β 2 ARF it + β 3 FST it + β 4 TVH it + ε it
where ICA = inside-city accidents, OCA = outside-city accidents, TAC = total number of accidents, ATM = average temperature, ARF = average rainfall, FST = frequency of sandstorms, TVH = total vehicles, i = region, t = time, ε = error term, α = intercept, and β = coefficient of the explanatory variables.
This article further estimated accidents leading to injury and death from different types of road traffic accidents, including empirical evidence from Saudi Arabia and using the following models:
TIJ it = α 0 + β 1 ICA it + β 2 OCA it + ε it
TIJ it = α 0 + β TAC it + ε it
TOD it = α 0 + β 1 TIJ it + β 2 ICA it + β 3 OCA it + ε it
TOD it = α 0 + β 1 TIJ it + β 2 TAC it + ε it
PDD it = α 0 + β 1 TIJ it + β 2 ICA it + β 3 OCA it + ε it
PDD it = α 0 + β 1 TIJ it + β 2 TAC it + + ε it
CYD it = α 0 + β 1 TIJ it + β 2 ICA it + β 3 OCA it + ε it
CYD it = α 0 + β 1 TIJ it + β 2 TAC it + ε it
MCD it = α 0 + β 1 TIJ it + β 2 ICA it + β 3 OCA it + ε it
MCD it = α 0 + β 1 TIJ it + β 2 TAC it + ε it
MVD it = α 0 + β 1 TIJ it + β 2 ICA it + β 3 OCA it + ε it
MVD it = α 0 + β 1 TIJ it + β 2 TAC it + ε it
where TIJ = total number of injuries, TOD = total number of deaths, PDD = pedestrian death, CYD = cyclist death, MCD = motorcyclist death, MVD = motor vehicle death.
To draw an inference, the study employed three static regression models: fixed effect (FE), random effect (RE), and the pooled ordinary least square (POLS) models. These three panel models have their own individual assumptions in modelling the relationship between a dependent variable and its predictors. For example, the POLS assumptions include neglecting the individual heterogeneity by imposing a common intercept and slope coefficient for all cross sections in the estimation. The FE model assumes the individual effect of intercepts that correlate with the explanatory variables, while the RE model assumes no correlation between individual effect and the predictor variables of the model. Therefore, to find out the best model, this study relied on the Hausman test.

4. Results

4.1. Descriptive Analysis

The descriptive statistics of the variables showed that, on average, in Saudi Arabia, TAC was 30,619, which is very alarming (Table 1). It was also seen that inside cities, the number of accidents was more than four times higher than outside cities. Moreover, on average, the number of injuries (2832) was four times higher than the number of deaths (696) at the time of occurrence of the accidents. Additionally, the highest number of deaths concerned motor vehicle drivers and passengers, followed by pedestrians, motorcyclists, and cyclists. As shown in Table 1, pedestrians were also highly affected by road accidents in Saudi Arabia. Besides, road accidents also included frequent crashes among motor vehicles, motor cyclists, and pedestrians.
On the other hand, the climatic variables, including temperature, rainfall and sandstorms, were also very hazardous in the city. During the study period, the average number of sandstorms in Saudi Arabia was 3.48 with a maximum of 14 events in a year. Similarly, heat was also very severe, since the average temperature during the study time was around 26 °C. The average rainfall was 4 in. in a year, which is very small compared to the other parts of the world. Though the average rainfall was very low, it was responsible for many RTAs in cities. The potential reasons could be the low quality of road construction, irregular road maintenance, irregular inspections of the drainage points and equipment, urban expansion, and heavy rain falls in short periods of time.

4.2. Regression Analysis

Based on the Hausman test [84], only the best-fit models (POLS, RE, or FE) are reported in the following tables and analyses. Table 2 displays the outcome of the models which investigated the impact of climate change on road traffic accidents in Saudi Arabia. The findings of model 1 revealed that the ATM, ARF, FST, and TVH caused statistically significant increases in the rate of ICA. Similarly, the regression model 2 revealed that the ATM and ARF caused a significant rise in the rate of OCA; on the other hand, the TVH and FST were not significant for the rate of OCA. Moreover, the outcome of model 3 indicated that ATM, ARF, FST, and TVH had positive and statistically significant impacts on the total number of accidents.
In addition, Table 3 displays the output of the models and shows that road traffic accidents caused injuries and deaths in Saudi Arabia. Models 4 and 5 indicated that ICA, OCA, and TAC caused statistically significant increases in the total number of injuries. As shown in model 6, the TIJ had a positive and statistically significant impact on the TOD. In the same model, it is observed that the inside city accidents (ICA) also had a positive and statistically significant impact on the TOD, but the OCA had no impact on the TOD. As shown in model 7, both the TIJ and the TAC had significant and positive impacts on related deaths.
Furthermore, Table 4 shows different types of road traffic accidents that led to deaths in Saudi Arabia. The regression models 8 and 9 revealed that TIJ, ICA, and OCA had no statistically significant impact on PDD. Moreover, the models 10 and 11 revealed that both TIJ and OCA had no significant impact on CYD, but ICA and TAC contributed significantly to the increase in the CYD.
Similarly, models 12 and 13 revealed that both ICA and TAC statistically and significantly increased the rate of MCD, but both TIJ and OCA did not have any significant impact on MCD. However, the models 14 and 15 found that except for OCA, TIJ, ICA, and TAC contributed statistically and significantly to increases in the rate of MVD.

5. Discussion

The purpose of this study was to assess, with an extensive statistical analysis, the impact of climate change on RTAs in Saudi Arabia due to changes in temperature, rainfall, and sandstorms. The study found that temperature, rainfall, sandstorms, and number of vehicles were responsible for the increase in the rate of road accidents.
The findings of this study revealed that an increase in the average temperature caused a statistically significant increase in the rate of RTAs, and ICAs were more frequent than OCAs. This result is supported by studies conducted by Yannis and Karlaftis [67], Basagana et al. [63], Al-Harbi et al. [66], Wåhlberg [68], Brijs et al. [69], Stipdonk [70], Hermans et al. [57]. A potential explanation for this result is related to the physiological or psychological effects of high temperatures on drivers, such as altered emotions, increased irritability, reaction time, and fatigue, and decreased concentration, all of which would have detrimental effects on drivers’ performance. Additionally, an increase in traffic volume during shiny hot days could be another potential reason for road accidents.
This study also has found a positive and significant relationship between the FSTs and the rate of road accidents. Therefore, this study supports the findings of the majority of previous studies [71,72,73,74,75,76,77,78,85,86]. For example, Al-Hemoud et al. [85] stated that sandstorm was a significant contributory factor to RTAs in Gulf Areas. Moreover, Maghrabi et al. [87] conducted a study on the impact of dust on road accidents in Saudi Arabia in March 2009 and found extensive damage to vehicles and a high number of RTAs. The possible reason for this result could be the poor visibility and traffic congestion produced by sand/dust storms. In addition, the lack of public alerts issued by relevant departments could be another potential reason for road accidents.
The results of this study also showed a positive and significant impact of rainfall on RTAs in Saudi Arabia. This finding is supported by many previous studies [45,52,53,54,55,56,57,58,59] reporting that RTAs increase during rainfall. For example, Bergel-Hayat et al. [54] reported that the risk of crash during rain was greater than in dry weather. Another study by Qayed [88] conducted in the city of Al-Ahsaa confirmed that in December, 13.7% of RTAs happened, and this percentage was considerably higher than in those of other months due to the higher frequency of rainfalls. In contrast, the lowest number of RTAs occurred in February, with a rate of 5.8%. As rainfall leads to wet, slick, and slippery roads, reduces visibility, increases humidity that clouds windows and windscreens, reduces the friction of the road surface, blinds drivers at night due to the reflection of the headlights of oncoming vehicles on the waterlogged roads during heavy rainfalls, it makes roads dangerous for motorists. These, all together, are the potential reason for the higher rate of RTAs.
Furthermore, both inside- and outside-city accidents significantly caused injuries, but only inside-city accident showed a positive and statistically significant impact on road traffic accident-related deaths. The majority of previous studies also support our findings by stating that the injury-related fatalities were deaths caused by road traffic accidents [89]. In addition, both inside- and outside-city accident were not statistically and significantly related to the death of the pedestrians, and only inside-city accident statistically and significantly caused cyclist, motorcyclist, and motor vehicle deaths. Furthermore, only in the case of motor vehicle accidents, the injuries caused by the accident later led to deaths. This finding related to cyclists is supported by many studies [90,91,92,93]. Moreover, most of the cyclist accidents occurred in urban areas because the cyclists collided with trucks and cars. The deaths caused by collisions were only motor vehicle deaths [94,95]. Similarly, the findings regarding motor vehicle deaths are also supported by other studies, like those of Cheng et al. [96], Jama et al. [97], and Lucidi et al. [98]. There are many measures that have been already taken to reduce RTAs in Saudi Arabia. For example, seat belt fastening is compulsory for the front seaters, and there will be a fine if the seat belt is not fastened. Moreover, speed cameras have been installed extensively in main cities, such as Jeddah and Riyadh, and the cities are controlled by the Saudi Traffic Police (STP). In addition, the STP can record road collisions and mortalities [13]. As a result, the reporting system of injuries and accidents has been substantially improved in recent years. Overall, there is still room for improvement in Saudi traffic road systems.

6. Conclusions

In Saudi Arabia, RTAs strongly contribute to health problems that influence the Saudi economy negatively and significantly. Based on our results, it is suggested that focusing on road traffic injuries is highly important to determine the reasons behind road accidents. Though road accidents can be investigated from many perspectives, the impact and importance of external factors, like climatic issues, on road accidents have received little attention. Climate change is causing a sharp increase in temperature and rate of sandstorms and is altering rainfall patterns in Saudi Arabia as in other parts of the world. To address such issue, this study has attempted to investigate the impacts of sandstorms, temperature, and rainfall on road traffic accidents in Saudi Arabia.
Considering climate issues, this study has found that temperature, rainfall, sandstorms, and number of vehicles were responsible for increasing RTAs in Saudi Arabia. It can be observed that climate change certainly poses risk to road safety in Saudi Arabia by increasing the number of road traffic accidents. However, due to a relatively small sample size and limited amount of available data, the findings of this study cannot be generalized to other Middle East and developing countries. This study also found that different types of RTAs led to injuries and deaths in Saudi Arabia.
To conclude, the negative impacts of climate change on road accidents can be mitigated through possible adaptation measures, as climate change is a slow phenomenon. This can be done through warning signs, roads improvements, and safety campaigns. Therefore, the impacts of increased temperature, altered rainfall patterns, and higher rate of sandstorms in Saudi Arabia can easily be reduced by increaed preparedness, traffic safety culture, and awareness at local and community levels of the impacts of climate change on road safety and of human vulnerability. The relevant authorities and driving training schools can play a major role in this regard. In addition, the use of modern information dissemination technologies, tools, and media might help to increase public awareness. The road infrastructure also needs to be designed in a manner that reduces waterlogging, considering that the return period of major extreme events is likely to decrease considerably. Moreover, the government of Saudi Arabia must do more for cyclists by assigning them paths and lanes. This means that the cycling infrastructure could be improved in both cities and rural areas. Moreover, in shared road environments, it is important to increase awareness among pedestrians, cyclists, and motorists, with appropriate programs. These courses of action to mitigate the negative impacts of climate will help not only to reduce RTAs, related injuries, deaths, and property damage but also to achieve sustainability in road transport.
Finally, the findings of this study will help policymakers to make effective traffic polices related to extreme-weather climatic issues in Saudi Arabia and other similar countries. Moreover, further research will help to overcome the limitations and to validate the findings of the study. We expect future research will explore the impact of climate change on RTAs using different sets of rainfall data, such as rainfall only on road areas or only heavy-frequency rainfall events, as well as data with more control variables, such as demographic, cultural, and socioeconomic features.

Author Contributions

M.M.I. had the original idea to explore the impact of climate change on RTAs in Saudi Arabia. M.M.I. and M.M.A. participated in the conception of the study, design of the analysis, writing of the results, and drafting of the manuscript. M.A. collected data and edited the English.

Acknowledgments

The project was funded by the Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, under grant no. (G: 173-849-1439). The authors, therefore, acknowledge with thanks DSR technical and financial support.

Conflicts of Interest

The authors declare no conflicts of interest.

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Table 1. Descriptive statistics of the variables a.
Table 1. Descriptive statistics of the variables a.
Variable NameObservationMeanStandard DeviationMinimum ValueMaximum Value
TODTotal number of deaths143695.729.02649739
PDDPedestrian death143134.556.55130154
CYDCyclist death14310.100.67912
MCDMotorcyclist death14329.401.622631
MVDMotor vehicle (drivers and passengers) death14352129.37462556
TIJTotal injuries1432832.303237.858814,932
ICAInside-city accidents14325,413.4437,220.81461165,853
OCAOutside-city accidents1435205.466372.812731,907
TACTotal number of accidents14330,61940,755.121103166,814
ATMAvg temperature143262.962033
ARFAvg rainfall1434.001.760.59.33
FSTFrequency of sandstorm1433.483.62014
TVHTotal number of vehicles143382,391.577,408.50275,385507,692
Source: a World Bank, Ministry of Interior and Presidency of Meteorology and Environment of Saudi Arabia, Our World in Data, and Global Health Data Exchange.
Table 2. Regression analysis for the prediction of inside-city accidents, outside-city accidents, and total accidents in relation to average temperature, average rainfall, frequency of sandstorms, and total vehicles.
Table 2. Regression analysis for the prediction of inside-city accidents, outside-city accidents, and total accidents in relation to average temperature, average rainfall, frequency of sandstorms, and total vehicles.
VariablesModel 1: (POLS) DV=ICAModel 2: (POLS) DV=OCAModel 3: (POLS) DV=TAC
ATM4691.04 ***688.86 ***5379.91 ***
(−931.59)(−163.32)(−1000.09)
ARF6960.86 ***1343.43 ***8304.3 ***
(−1567.65)(−274.83)(−1682.91)
FST1610.9 **22.691633.6 ***
(−759.14)(−133.09)(−814.95)
TVH0.086 ***−0.0120.099 **
(−0.035)(−0.006)(−0.038)
Constant474.431.73306.1
Note: The values in parentheses represent the error; the symbols **, *** stand for significant probability value at p < 0.05 and <0.01, respectively.
Table 3. Regression analysis for the prediction of total injuries, total number of deaths by total injuries, inside-city accidents, outside-city accidents, and total number of accidents
Table 3. Regression analysis for the prediction of total injuries, total number of deaths by total injuries, inside-city accidents, outside-city accidents, and total number of accidents
VariablesModel 4: (RE) DV=TIJModel 5: (FE) DV=TIJModel 6: (FE) DV=TODModel 7: (FE) DV=TOD
TIJ 0.004 **0.004 **
(−0.002)(−0.001)
ICA0.011 ** 0.0006 ***
(−0.005) (−0.0001)
OCA0.124 *** 0.001
(−0.028) (−0.0007)
TAC 0.017 ** 0.0006 ***
(−0.005)(−0.00001)
Constant1883.12283.3661.89662.33
Note: The values in parentheses represent the error; the symbols **, *** stand for significant probability value at p < 0.05 and <0.01, respectively.
Table 4. Regression analysis for the prediction of pedestrian death, cyclist death, motorcyclists death, and motor vehicle death by total injuries, inside-city accidents, outside-city accidents, and total number of accidents.
Table 4. Regression analysis for the prediction of pedestrian death, cyclist death, motorcyclists death, and motor vehicle death by total injuries, inside-city accidents, outside-city accidents, and total number of accidents.
VariablesModel 8: (POLS)
DV=PDD
Model 9: (POLS)
DV=PDD
Model 10: (FE)
DV=CYD
Model 11: (FE)
DV=CYD
Model 12:(FE)
DV=MCD
Model 13: (FE)
DV=MCD
Model 14: (FE)
DV=MVD
Model 15: (FE)
DV=MVD
TIJ−0.00005
(0.0002)
−0.00004
(0.0002)
6.751
(0.0005)
0.00002
(0.00004)
0.0001
(0.0001)
0.0001
(0.00009)
0.004 **
(0.002)
0.004 **
(0.001)
ICA−9.070
(0.0001)
6.771 *
(3.460)
0.0003 ***
(6.89)
0.0006 ***
(0.0001)
OCA7.560
(0.0001)
0.00002
(0.00001)
0.00002
(0.00003)
0.001
(0.0007)
TAC −7.890
(0.00001)
7.92 **
(3.25)
0.00003 ***
(0.0001)
0.0006 ***
(0.0001)
Constant134.79134.819.8469.86428.1428.13486.17486.52
Note: The values in parentheses represent the error; the symbols *, **, *** stand for significant probability value at p < 0.10, <0.05, and <0.01, respectively.

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